what is machine learning ?
Machine Learning is the science of teaching machines to learn and behave like people, and to refine their learning over time in an autonomous manner, using evidence and knowledge from experiments and real-world experiences.
We Learnbay offers top trending courses in the area of data science courses such as artificial intelligence, machine learning, deep learning and data science and so on.
A chat application that provides real-time recommendations when user discusses about movies, restaurants, products, travel etc. on chat. The application works on user click tracking and explicit user feedback to learn preferences and personalize future recommendations and improve classification accuracy.
https://github.com/rishirdua/smart-chat
what is machine learning ?
Machine Learning is the science of teaching machines to learn and behave like people, and to refine their learning over time in an autonomous manner, using evidence and knowledge from experiments and real-world experiences.
We Learnbay offers top trending courses in the area of data science courses such as artificial intelligence, machine learning, deep learning and data science and so on.
A chat application that provides real-time recommendations when user discusses about movies, restaurants, products, travel etc. on chat. The application works on user click tracking and explicit user feedback to learn preferences and personalize future recommendations and improve classification accuracy.
https://github.com/rishirdua/smart-chat
This is one of the most common questions that most of you will have in your mind. If you are not aware of the GRE Exam syllabus, you tend to cram through all the topics possibly known to you.
A scoring rubric for automatic short answer grading systemTELKOMNIKA JOURNAL
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During the past decades, researches about automatic grading have become an interesting issue. These studies focuses on how to make machines are able to help human on assessing studentsâ learning outcomes. Automatic grading enables teachers to assess student's answers with more objective, consistent, and faster. Especially for essay model, it has two different types, i.e. long essay and short answer. Almost of the previous researches merely developed automatic essay grading (AEG) instead of automatic short answer grading (ASAG). This study aims to assess the sentence similarity of short answer to the questions and answers in Indonesian without any language semantic's tool. This research uses pre-processing steps consisting of case folding, tokenization, stemming, and stopword removal. The proposed approach is a scoring rubric obtained by measuring the similarity of sentences using the string-based similarity methods and the keyword matching process. The dataset used in this study consists of 7 questions, 34 alternative reference answers and 224 studentâs answers. The experiment results show that the proposed approach is able to achieve a correlation value between 0.65419 up to 0.66383 at Pearson's correlation, with Mean Absolute Error (íí´í¸) value about 0.94994 until 1.24295. The proposed approach also leverages the correlation value and decreases the error value in each method.
Machine Learning Techniques with Ontology for Subjective Answer Evaluationijnlc
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Computerized Evaluation of English Essays is performed using Machine learning techniques like Latent
Semantic Analysis (LSA), Generalized LSA, Bilingual Evaluation Understudy and Maximum Entropy.
Ontology, a concept map of domain knowledge, can enhance the performance of these techniques. Use of
Ontology makes the evaluation process holistic as presence of keywords, synonyms, the right word
combination and coverage of concepts can be checked. In this paper, the above mentioned techniques are
implemented both with and without Ontology and tested on common input data consisting of technical
answers of Computer Science. Domain Ontology of Computer Graphics is designed and developed. The
software used for implementation includes Java Programming Language and tools such as MATLAB,
ProtĂŠgĂŠ, etc. Ten questions from Computer Graphics with sixty answers for each question are used for
testing. The results are analyzed and it is concluded that the results are more accurate with use of
Ontology.
10,00 Modelling and analysis of geophysical data using geostatistics and mach...Beniamino Murgante
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10,00 Modelling and analysis of geophysical data using geostatistics and machine learning
Vasily Demyanov â HeriotâWatt Institute, Edinburgh (U.K.)
Intelligent Analysis of Environmental Data (S4 ENVISA Workshop 2009)
This is one of the most common questions that most of you will have in your mind. If you are not aware of the GRE Exam syllabus, you tend to cram through all the topics possibly known to you.
A scoring rubric for automatic short answer grading systemTELKOMNIKA JOURNAL
Â
During the past decades, researches about automatic grading have become an interesting issue. These studies focuses on how to make machines are able to help human on assessing studentsâ learning outcomes. Automatic grading enables teachers to assess student's answers with more objective, consistent, and faster. Especially for essay model, it has two different types, i.e. long essay and short answer. Almost of the previous researches merely developed automatic essay grading (AEG) instead of automatic short answer grading (ASAG). This study aims to assess the sentence similarity of short answer to the questions and answers in Indonesian without any language semantic's tool. This research uses pre-processing steps consisting of case folding, tokenization, stemming, and stopword removal. The proposed approach is a scoring rubric obtained by measuring the similarity of sentences using the string-based similarity methods and the keyword matching process. The dataset used in this study consists of 7 questions, 34 alternative reference answers and 224 studentâs answers. The experiment results show that the proposed approach is able to achieve a correlation value between 0.65419 up to 0.66383 at Pearson's correlation, with Mean Absolute Error (íí´í¸) value about 0.94994 until 1.24295. The proposed approach also leverages the correlation value and decreases the error value in each method.
Machine Learning Techniques with Ontology for Subjective Answer Evaluationijnlc
Â
Computerized Evaluation of English Essays is performed using Machine learning techniques like Latent
Semantic Analysis (LSA), Generalized LSA, Bilingual Evaluation Understudy and Maximum Entropy.
Ontology, a concept map of domain knowledge, can enhance the performance of these techniques. Use of
Ontology makes the evaluation process holistic as presence of keywords, synonyms, the right word
combination and coverage of concepts can be checked. In this paper, the above mentioned techniques are
implemented both with and without Ontology and tested on common input data consisting of technical
answers of Computer Science. Domain Ontology of Computer Graphics is designed and developed. The
software used for implementation includes Java Programming Language and tools such as MATLAB,
ProtĂŠgĂŠ, etc. Ten questions from Computer Graphics with sixty answers for each question are used for
testing. The results are analyzed and it is concluded that the results are more accurate with use of
Ontology.
10,00 Modelling and analysis of geophysical data using geostatistics and mach...Beniamino Murgante
Â
10,00 Modelling and analysis of geophysical data using geostatistics and machine learning
Vasily Demyanov â HeriotâWatt Institute, Edinburgh (U.K.)
Intelligent Analysis of Environmental Data (S4 ENVISA Workshop 2009)
6th International Disaster and Risk Conference IDRC 2016 Integrative Risk Management - Towards Resilient Cities. 28 August - 01 September 2016 in Davos, Switzerland
Hazards Risk and Perils, a complete explanation .Sagar Garg
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A complete description of risk hazard and perils with gif pictures.
download it for the best view. as gif images on work when it was run on power point platform
E-Learning Student Assistance Model for the First Computer Programming CourseIJITE
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E-Learning applied to computer programming course design is a promising area of research. The student having clear understanding of the programming constructs can apply it to solve various problems. Because of limited time and availability, the instructor can go back to some extent to cover the weaknesses of their students that hinder the understanding of the problems. As more lessons are covered, the weak students become weaker in programming. To cope up with these problems an e-learning system is devised which the student can use anywhere and at any time as a web application. It comprises of both tutoring and assessment and also provides guiding the students to error correction using back-tracking technique to refine the concepts and reattempt the programming problem.
E-LEARNING STUDENT ASSISTANCE MODEL FOR THE FIRST COMPUTER PROGRAMMING COURSE IJITE
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E-Learning applied to computer programming course design is a promising area of research. The student
having clear understanding of the programming constructs can apply it to solve various problems. Because
of limited time and availability, the instructor can go back to some extent to cover the weaknesses of their
students that hinder the understanding of the problems. As more lessons are covered, the weak students
become weaker in programming. To cope up with these problems an e-learning system is devised which the
student can use anywhere and at any time as a web application. It comprises of both tutoring and
assessment and also provides guiding the students to error correction using back-tracking technique to
refine the concepts and reattempt the programming problem
E-Learning Student Assistance Model for the First Computer Programming CourseIJITE
Â
E-Learning applied to computer programming course design is a promising area of research. The student
having clear understanding of the programming constructs can apply it to solve various problems. Because
of limited time and availability, the instructor can go back to some extent to cover the weaknesses of their
students that hinder the understanding of the problems. As more lessons are covered, the weak students
become weaker in programming. To cope up with these problems an e-learning system is devised which the
student can use anywhere and at any time as a web application. It comprises of both tutoring and
assessment and also provides guiding the students to error correction using back-tracking technique to
refine the concepts and reattempt the programming problem.
Basics of QuotingA guideline for good quoting is to integrate.docxJASS44
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Basics of Quoting
A guideline for good quoting is to integrate the quote into your own writing. Be sure to set up a quote with proper context, such as who said the quote, and any background information required to understand what that person is talking about. This quote set-up should go before the quote, so the reader isnât wondering whoâs talking when you start a quote. Ideally, you should be able to put the quote inside your own sentence, rather than having the quote stand alone.
Level One: Summarize, then Quote
If you canât include the quote in your own sentence, at the very least you should prepare the reader for a quote by giving a brief summary before the quote. For instance:
Mr. Fleharty argues that quotes should fit smoothly in your own sentences. âThe more you can integrate a quote in your own writing, the better.â
Level Two: Using Set-up Phrases
This can get a little trickier with punctuation and proper verb tense, but you should be able to attribute a quote to somebody with a short phrase provided before the quote, in the same sentence. In MLA format, these signal phrases should use present tense verbs.
According to Mr. Fleharty, âThe more you can integrate a quoteâŚthe better.â
In âBasics of Quoting,â Mr. Fleharty says, â--------------------------.â
Be careful to avoid the common mistakes that come up when using these phrases. For instance, if you use âAccording to X,â you donât need to add âX states/believes/says _____.â They mean the same thing. Also, avoid âAccording to the article, it says _________.â This shouldnât happen- name the author instead, or at the very least the website or magazine the article is from.
Level Three: Mid-Sentence Quotes
The best way to integrate quotes into your own essay is to quote small phrases from the source as parts of your own sentence. Essentially, you are summarizing or analyzing what the author is saying WHILE using some of their own words. Be absolutely sure the sentence still flows grammatically. Picture the sentence without the quote marks. If necessary, you can change parts of the quote by using [brackets] to let readers know youâve changed it.
Mr. Fleharty argues that you should âintegrate a quote in your own writingâ to ensure that quotes arenât just standing around adding nothing to your essay.
One common mistake when starting to use this method is quoting too little to be worthwhile. For instance, donât just quote one word unless itâs crucial that the author is using that specific word. Try to take whole phrases at a time to make it worth quoting, otherwise just stick to summarizing the source instead.
Ultimately, quoting successfully comes down to providing context and integrating the quotes into your own writing. In other words, remember to set up your quotes.
Assignment
Read an article with a clearly named author and write a response to it that uses five quotes from the original. Use a different form of quote set-up for each quote- donât repeat the same one for a ...
Presentation on Teaching Quantitative Methods using Excel workbook courseware. -- at the 25th International Conference on Technology in Collegiate Mathematics, Boston, March 21 - 24, 2013. A Pearson Education event.
GCU College of EducationLESSON PLAN TEMPLATESection 1 LessoMatthewTennant613
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GCU College of Education
LESSON PLAN TEMPLATE
Section 1: Lesson Preparation
Teacher Candidate Name:
Grade Level:
Date:
Unit/Subject:
Instructional Plan Title:
Lesson Summary and Focus:
In 2-3 sentences, summarize the lesson, identifying the central focus based on the content and skills you are teaching.
Classroom and Student Factors/Grouping:
Describe the important classroom factors (demographics and environment) and student factors (IEPs, 504s, ELLs, students with behavior concerns, gifted learners), and the effect of those factors on planning, teaching, and assessing students to facilitate learning for all students. This should be limited to 2-3 sentences and the information should inform the differentiation components of the lesson.
National/State Learning Standards:
Review national and state standards to become familiar with the standards you will be working with in the classroom environment.
Your goal in this section is to identify the standards that are the focus of the lesson being presented. Standards must address learning initiatives from one or more content areas, as well as align with the lessonâs learning targets/objectives and assessments.
Include the standards with the performance indicators and the standard language in its entirety.
Specific Learning Target(s)/Objectives:
Learning objectives are designed to identify what the teacher intends to measure in learning. These must be aligned with the standards. When creating objectives, a learner must consider the following:
¡ Who is the audience
¡ What action verb will be measured during instruction/assessment
¡ What tools or conditions are being used to meet the learning
What is being assessed in the lesson must align directly to the objective created. This should not be a summary of the lesson, but a measurable statement demonstrating what the student will be assessed on at the completion of the lesson. For instance, âunderstandâ is not measureable, but âdescribeâ and âidentifyâ are.
For example:
Given an unlabeled map outlining the 50 states, students will accurately label all state names.
Academic Language
In this section, include a bulleted list of the general academic vocabulary and content-specific vocabulary you need to teach. In a few sentences, describe how you will teach students those terms in the lesson.
Resources, Materials, Equipment, and Technology:
List all resources, materials, equipment, and technology you and the students will use during the lesson. As required by your instructor, add or attach copies of ALL printed and online materials at the end of this template. Include links needed for online resources.
Section 2: Instructional Planning
Anticipatory Set
Your goal in this section is to open the lesson by activating studentsâ prior knowledge, linking previous learning with what they will be learning in this lesson and gaining student interest for the lesson. Consider various learning preferences (movement, mus ...
An Adaptive Approach for Subjective Answer Evaluationvivatechijri
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In current academic environment, assignments and home works are very necessary so that students
can increase their final grades. This assignments and home works are checked manually by teachers, due to this
it consumes lots of time and efforts. Due to manual checking sometimes human error may occur which may affect
to studentâs grades. Students may misplace their hard copies of assignments because of this they have to rewrite
it again. In order to overcome these problems, the proposed system will convert the manual work to digital, in
which student will submit their assignments to the system and the system will generate and assign appropriate
grades. In the proposed system, by using K Nearest Neighbor Algorithm, it will collect keywords check for the
similarity and will generate similarity score. It will also check the relation of the keyword with respect to sentence.
To comparing the keywords with synonyms and similar meaning words Semantic Similarity Measure algorithm
will be used. After getting the similarity score the grades are assigned accordingly.
June presentations org_adoption_learning_analyticsShane Dawson
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Learning analytics (LA) has been touted as a game changer for education. The rapidly growing literature associated with the field serves to promote this fervour in citing the vast impact LA can and will play in the education space. From the detection of at-risk students to address retention and performance, building self-regulated learning, development and identification of 21st Century literacies to the realisation of personalised learning, there appears little that LA cannot contribute to within learning and teaching practice. However, if LA is such an impactful, desirable and worthy endeavour that can effectively improve learning, and our understanding of the learning process, why are there so few examples of institutional LA adoption?
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Model Attribute Check Company Auto PropertyCeline George
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In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Operation âBlue Starâ is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
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Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
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Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
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This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
3. The Idea
ďApply sentence compression to summarize student responses to short answer
questions
ďGraph-based sentence compression algorithms have been developed and
applied to summarize multiple related sentences and opinions within textual
reviews.
4. The Idea
ďStudent responses are made up of short sentences with common phrases
being used
ďStudent responses are an ideal candidate for sentence compression because
there is high similarity and redundancy between student responses
ďVery similar to the summarisation of product reviews
5. Proposed Visual Analytics Tool
ďLecturers and tutors require a way to analyse and visualize student responses
so that they can:
ďunderstand how students have responded to a question
ďreview the vocabulary being used
ďidentify knowledge gaps
ďprovide feedback to groups of students with similar knowledge gaps
6. Proposed Visual Analytics Tool
ďInitially, it was not the aim to automatically grade short answer questions
ďRather we are building visual analytics to help lecturers and tutors provide
feedback
7. The Sentence Compression Algorithm
ďThe âFilippovaâ algorithm summarizes similar or related sentences and outputs
into a single short sentence that summarizes the most salient theme conveyed
in the cluster of sentences.
ďThe algorithm constructs a word graph and uses an approach based upon the
shortest paths between words in the graph to produce a summary sentence.
ď Only sentence tokenization and part of speech tagged is required (i.e. no
hand crafted rules).
ďAll of the words contained in the sentences form the nodes in the word graph.
ďA word graph is a directed graph where an edge from word A to word B
represents an adjacency relation.
8. The Sentence Compression Algorithm
ďEdges within the word graph are used to connect words that are adjacent in a
sentence with the edge weight incremented by 1 each time a word occurs after another
in a sequence.
ďGood sentence compression goes through all the nodes which represent important
concepts but does not pass the same node several times.
ďThis is achieved by inverting the edge weights and finding the K shortest paths from
the start to the end node in the word graph that don't include a verb.
ďThe path through the graph with the minimum total weight is selected as the summary
sentence.
ďAdditional graph scoring and ranking metrics are used to take into consideration strong
links between words and determine salient words.
9. 3 Approaches to Visual Analytics
ďApproach 1: Display the K candidate sentences that are derived from the Filippova algorithm.
This is only a textual display of the sentences.
ďApproach 2: Construct a graph from the K candidate sentences and use a graph layout
algorithm to display the graph is a visual manner. The advantage over the textual display of the
sentence is that loops of words and branches between words would be more easily identifiable.
ďApproach 3: Display the full word graph and highlight the K candidate paths (sentences) on the
graph display. This visualization would allow the lecturer/tutor to see the range of words used.
10. Example
Question: What are the main advantages associated with object-oriented programming?
Teachers Answer: Abstraction and reusability.
Score Candidate Summary Sentence
0.025 existing classes can be reused program.
0.025 existing classes can be reused program maintenance.
0.023 existing classes can be reused program maintenance and verification are easier.
0.042 objects can be reused program maintenance.
0.04
existing classes can be reused and program.
0.038 existing classes can be reused and program maintenance.
0.05 the classes can be reused program .
0.034 objects can be reused program maintenance and verification are easier.
0.047 the classes can be reused program maintenance.
0.059 objects can be reused and program.
Very few students include "abstraction" in their answer or concepts that would be associated with âabstractionâ such as
âencapsulationâ. Most students mention reusability and maintenance/debugging but it is actually âabstractionâ that leads to easier
maintenance/debugging of object oriented programming code.
The proposed visualizations would therefore allow the lecturer/tutor to identify the concepts that the students have missed or
explained incorrectly and guide the lecturer in providing feedback.
11. Next Steps
ďA between subjects comparative study is being planned.
ďThe study will be comprised of two groups.
ďGroup A will be required to read all student responses and identify student knowledge gaps.
ďGroup B will use the visualizations produced from the output of sentence compression to identify
knowledge gaps in the student responses.
ďIdentified knowledge gaps from Group A and Group B will then be compared.
ďParticipants in Group B will be shown all 3 approaches described in Section 3 and asked to rate each
approach based on principles of visual analytics.
ďOpen to advice on evaluation from a visual analytics perspective?
12. Next Steps
ďLinking between the perceived ârightâ answer and student feedback
ďVisualisations of network with number of student responses associated
ďVisualisation and integration of feedback prompts based on student entry
Editor's Notes
----- Meeting Notes (17/03/15 05:21) -----
Filippova algorithm summarises similar or related